Differential phospho/protein analysis - CRC Batch 1
Sophie Herbst
Analysis date: 2023-02-03
Depends on
DIPG_FirstBatch_DataProcessing Script
load("../Data/Cache/Xenografts_Batch1_2_DataProcessing.RData")TODO
- Do differential abudance analysis for prep batch and mass spec run
Setup
Load libraries and functions
Analysis
DEP
Tyrosine all
Each condition vs ctrl
data_diff_ctrl_vs_E_pY <- test_diff(pY_se, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY") )## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(comparison)
##
## # Now:
## data %>% select(all_of(comparison))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.## 'select()' returned 1:1 mapping between keys and columns## Loading required namespace: reactome.db## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.9766082 0.9903782
## 2: ABC-family proteins mediated transport 0.9766082 0.9903782
## 3: ADP signalling through P2Y purinoceptor 1 0.3469786 0.9003641
## 4: ALK mutants bind TKIs 0.4113060 0.9003641
## 5: APC/C-mediated degradation of cell cycle proteins 0.4990215 0.9003641
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.3251534 0.9003641
## log2err ES NES size leadingEdge
## 1: 0.04558782 0.5093168 0.6698577 1 5687
## 2: 0.04558782 0.5093168 0.6698577 1 5687
## 3: 0.09821234 0.8509317 1.1191525 1 1432
## 4: 0.08835944 0.8198758 1.0783075 1 1213
## 5: 0.07808923 -0.6702564 -1.0045051 2 983
## 6: 0.10512513 -0.8447205 -1.1264660 1 983data_diff_EC_vs_ctrl_pY <- test_diff(pY_se, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY") )## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.8928571 0.9514509
## 2: ABC-family proteins mediated transport 0.8928571 0.9514509
## 3: ADP signalling through P2Y purinoceptor 1 0.2752101 0.7369183
## 4: ALK mutants bind TKIs 1.0000000 1.0000000
## 5: APC/C-mediated degradation of cell cycle proteins 0.7783172 0.9514509
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.3802281 0.8086806
## log2err ES NES size leadingEdge
## 1: 0.05312981 0.5527950 0.7399919 1 5687
## 2: 0.05312981 0.5527950 0.7399919 1 5687
## 3: 0.11776579 0.8757764 1.1723468 1 1432
## 4: 0.04336370 -0.5031056 -0.6704875 1 1213
## 5: 0.04744832 -0.5292285 -0.8277662 2 983
## 6: 0.09139243 -0.8136646 -1.0843687 1 983Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.3151751 0.5351808
## 2: ABC-family proteins mediated transport 0.3151751 0.5351808
## 3: ADP signalling through P2Y purinoceptor 1 0.7334630 0.8929530
## 4: ALK mutants bind TKIs 0.7008197 0.8656095
## 5: APC/C-mediated degradation of cell cycle proteins 0.6565934 0.8386815
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.9649805 0.9805038
## log2err ES NES size leadingEdge
## 1: 0.10395847 0.8571429 1.1230369 1 5687
## 2: 0.10395847 0.8571429 1.1230369 1 5687
## 3: 0.05871859 0.6583851 0.8626225 1 1432
## 4: 0.06335970 -0.6645963 -0.8825751 1 1213
## 5: 0.08152651 0.5312500 0.8614477 2 5687
## 6: 0.04604577 0.5279503 0.6917256 1 983Not ctrl vs ctrl
data_diff_vs_ctrl_pY <- test_diff_to_all_other(pY_se, control = "ctrl")
dep_vs_ctrl_pY <- add_rejections_SH(data_diff_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_vs_ctrl_pY, contrast = "not_ctrl_vs_ctrl", label_size = 2, add_names = TRUE, additional_title = "pY") )Return_DEP_Hits_Plots(data = pY_noNA, dep_vs_ctrl_pY, comparison = "not_ctrl_vs_ctrl_diff")EC vs E
data_diff_EC_vs_E_pY <- test_diff(pY_se, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_E_pY, contrast = "EC_vs_E", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.9818548 0.9935436
## 2: ABC-family proteins mediated transport 0.9818548 0.9935436
## 3: ADP signalling through P2Y purinoceptor 1 0.8951613 0.9935436
## 4: ALK mutants bind TKIs 0.7628458 0.9510145
## 5: APC/C-mediated degradation of cell cycle proteins 0.6895735 0.9081003
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.5080645 0.7877949
## log2err ES NES size leadingEdge
## 1: 0.04688199 0.5155280 0.6881103 1 5687
## 2: 0.04688199 0.5155280 0.6881103 1 5687
## 3: 0.05111480 0.5527950 0.7378532 1 1432
## 4: 0.05760911 -0.6149068 -0.8181550 1 1213
## 5: 0.07130530 0.5187500 0.8275992 2 983
## 6: 0.07871138 0.7515528 1.0031487 1 983#data_results <- get_df_long(dep)EBC vs EC
data_diff_EC_vs_EBC_pY <- test_diff(pY_se, type = "manual",
test = c("EC_vs_EBC"))## Tested contrasts: EC_vs_EBCdep_EC_vs_EBC_pY <- add_rejections_SH(data_diff_EC_vs_EBC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_EBC_pY, contrast = "EC_vs_EBC", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_EBC_pY, comparison = "EC_vs_EBC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.17063492 0.4446525
## 2: ABC-family proteins mediated transport 0.17063492 0.4446525
## 3: ADP signalling through P2Y purinoceptor 1 0.67269076 0.7901688
## 4: ALK mutants bind TKIs 0.31927711 0.5359499
## 5: APC/C-mediated degradation of cell cycle proteins 0.03894189 0.3034222
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.25793651 0.4852412
## log2err ES NES size leadingEdge
## 1: 0.14920754 -0.9192547 -1.2016982 1 5687
## 2: 0.14920754 -0.9192547 -1.2016982 1 5687
## 3: 0.06435834 0.6708075 0.8850999 1 1432
## 4: 0.10512513 0.8509317 1.1227656 1 1213
## 5: 0.32177592 -0.8937500 -1.4466064 2 5687,983
## 6: 0.11828753 -0.8881988 -1.1611003 1 983#data_results <- get_df_long(dep)Preps
pY_se_preps <- pY_se
pY_se_preps$treatment <- pY_se_preps$condition
pY_se_preps$prep <- unlist(prep_l[paste0("Xenograft_", rownames(colData(pY_se_preps)) )])
pY_se_preps_comp <- pY_se_preps
pY_se_preps_comp$condition <- pY_se_preps$prep
data_diff_prep1_vs_prep2_pY <- test_diff(pY_se_preps_comp, type = "manual",
test = c("prep1_vs_prep2"))## Tested contrasts: prep1_vs_prep2dep_prep1_vs_prep2_pY <- add_rejections_SH(data_diff_prep1_vs_prep2_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_prep1_vs_prep2_pY, contrast = "prep1_vs_prep2", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_prep1_vs_prep2_pY, comparison = "prep1_vs_prep2_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.54635108 0.7414198
## 2: ABC-family proteins mediated transport 0.54635108 0.7414198
## 3: ADP signalling through P2Y purinoceptor 1 0.87272727 0.9189189
## 4: ALK mutants bind TKIs 0.06262626 0.2870370
## 5: APC/C-mediated degradation of cell cycle proteins 0.37731959 0.7141142
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.51479290 0.7414198
## log2err ES NES size leadingEdge
## 1: 0.07380527 -0.7329193 -0.9802338 1 5687
## 2: 0.07380527 -0.7329193 -0.9802338 1 5687
## 3: 0.05237591 0.5652174 0.7453080 1 1432
## 4: 0.25720647 0.9689441 1.2776709 1 1213
## 5: 0.09656296 -0.7375000 -1.1330785 2 983,5687
## 6: 0.07687367 -0.7453416 -0.9968479 1 983#data_results <- get_df_long(dep)Runs
pY_se_runs <- pY_se
pY_se_runs$treatment <- pY_se_runs$condition
pY_se_runs$run <- str_split(colnames(pY_se_runs), pattern = "_", simplify = TRUE)[,4]
pY_se_runs_comp <- pY_se_runs
pY_se_runs_comp$condition <- pY_se_runs$run
data_diff_Set1_vs_Set2_pY <- test_diff(pY_se_runs_comp, type = "manual",
test = c("Set1_vs_Set2"))## Tested contrasts: Set1_vs_Set2dep_Set1_vs_Set2_pY <- add_rejections_SH(data_diff_Set1_vs_Set2_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_Set1_vs_Set2_pY, contrast = "Set1_vs_Set2", label_size = 2, add_names = TRUE, additional_title = "pY") )## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_Set1_vs_Set2_pY, comparison = "Set1_vs_Set2_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: ABC transporter disorders 0.4891089 0.7661997
## 2: ABC-family proteins mediated transport 0.4891089 0.7661997
## 3: ADP signalling through P2Y purinoceptor 1 0.8229376 0.9308113
## 4: ALK mutants bind TKIs 0.1106640 0.4497354
## 5: APC/C-mediated degradation of cell cycle proteins 0.4334862 0.7661997
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.7049505 0.8763396
## log2err ES NES size leadingEdge
## 1: 0.07977059 -0.7453416 -0.9866071 1 5687
## 2: 0.07977059 -0.7453416 -0.9866071 1 5687
## 3: 0.05490737 0.5900621 0.7849932 1 1432
## 4: 0.19002331 0.9503106 1.2642522 1 1213
## 5: 0.09466462 -0.6750000 -1.0631931 2 5687,983
## 6: 0.06143641 -0.6708075 -0.8879464 1 983## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
#data_results <- get_df_long(dep)Prep 1
Each condition vs ctrl
pY_se_prep1 <- pY_se_preps
pY_se_prep1 <- pY_se_prep1[,pY_se_prep1$prep == "prep1" ]
data_diff_ctrl_vs_E_pY <- test_diff(pY_se_prep1, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modepaste0("log2FC_Xenograft_", colnames(pY_se_prep1) )## [1] "log2FC_Xenograft_EC_24h_2_Set1" "log2FC_Xenograft_EC_24h_5_Set1"
## [3] "log2FC_Xenograft_E_24h_4_Set1" "log2FC_Xenograft_EC_24h_1_Set1"
## [5] "log2FC_Xenograft_EBC_24h_3_Set1" "log2FC_Xenograft_ctrl_5d_3_Set1"
## [7] "log2FC_Xenograft_E_24h_1_Set1" "log2FC_Xenograft_E_24h_2_Set1"
## [9] "log2FC_Xenograft_EBC_24h_2_Set1" "log2FC_Xenograft_EBC_24h_4_Set1"
## [11] "log2FC_Xenograft_E_24h_5_Set1" "log2FC_Xenograft_E_24h_3_Set1"
## [13] "log2FC_Xenograft_EC_24h_4_Set1" "log2FC_Xenograft_EC_24h_3_Set1"
## [15] "log2FC_Xenograft_EBC_24h_1_Set1" "log2FC_Xenograft_ctrl_5d_3_Set2"str_remove( colnames(pY_noNA), "log2FC_Xenograft_" )## [1] "Annotated_Sequence" "HGNC_Symbol"
## [3] "EC_24h_2_Set1" "EC_24h_5_Set1"
## [5] "E_24h_4_Set1" "ctrl_5d_7_Set1"
## [7] "EC_24h_1_Set1" "EBC_24h_3_Set1"
## [9] "ctrl_5d_3_Set1" "E_24h_1_Set1"
## [11] "E_24h_2_Set1" "EBC_24h_2_Set1"
## [13] "EBC_24h_4_Set1" "E_24h_5_Set1"
## [15] "E_24h_3_Set1" "ctrl_5d_5_Set1"
## [17] "EC_24h_4_Set1" "EC_24h_3_Set1"
## [19] "EBC_24h_1_Set1" "EBC_5d_4_Set2"
## [21] "ctrl_5d_7_Set2" "EC_5d_3_Set2"
## [23] "ctrl_5d_5_Set2" "E_5d_2_Set2"
## [25] "EBC_5d_3_Set2" "E_5d_3_Set2"
## [27] "EBC_5d_2_Set2" "ctrl_5d_3_Set2"
## [29] "E_5d_4_Set2" "EC_5d_4_Set2"
## [31] "E_5d_5_Set2" "EC_5d_2_Set2"
## [33] "EC_5d_1_Set2" "EBC_5d_1_Set2"
## [35] "EC_5d_5_Set2" "EBC_5d_5_Set2"
## [37] "Master.Protein.Accessions" "Master.Protein.Descriptions"pY_noNA %>% select()## # A tibble: 231 × 0Return_DEP_Hits_Plots(data = pY_noNA ,
dep_ctrl_vs_E_pY,
comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: ABC transporter disorders 0.7454910 0.9111018
## 2: ABC-family proteins mediated transport 0.7454910 0.9111018
## 3: ADP signalling through P2Y purinoceptor 1 0.1903808 0.9111018
## 4: ALK mutants bind TKIs 0.3366733 0.9111018
## 5: APC/C-mediated degradation of cell cycle proteins 0.7447217 0.9111018
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.9438878 0.9771684
## log2err ES NES size leadingEdge
## 1: 0.05934877 0.6459627 0.8520523 1 5687
## 2: 0.05934877 0.6459627 0.8520523 1 5687
## 3: 0.14122512 0.9192547 1.2125360 1 1432
## 4: 0.10171390 0.8385093 1.1060294 1 1213
## 5: 0.05736674 0.5437500 0.8123386 2 5687,983
## 6: 0.04840876 0.5403727 0.7127745 1 983## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
Return_DEP_Hits_Plots(data = pY_noNA ,
dep_ctrl_vs_E_pY,
comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: ABC transporter disorders 0.7338710 0.9137037
## 2: ABC-family proteins mediated transport 0.7338710 0.9137037
## 3: ADP signalling through P2Y purinoceptor 1 0.1975806 0.9137037
## 4: ALK mutants bind TKIs 0.3447581 0.9137037
## 5: APC/C-mediated degradation of cell cycle proteins 0.7348928 0.9137037
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.9314516 0.9687697
## log2err ES NES size leadingEdge
## 1: 0.06037864 0.6459627 0.8533917 1 5687
## 2: 0.06037864 0.6459627 0.8533917 1 5687
## 3: 0.13880511 0.9192547 1.2144420 1 1432
## 4: 0.10063339 0.8385093 1.1077681 1 1213
## 5: 0.05871859 0.5437500 0.8157366 2 5687,983
## 6: 0.04929177 0.5403727 0.7138950 1 983## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_prep1, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: ABC transporter disorders 0.8000000 0.9676585
## 2: ABC-family proteins mediated transport 0.8000000 0.9676585
## 3: ADP signalling through P2Y purinoceptor 1 0.1341223 0.9676585
## 4: ALK mutants bind TKIs 0.3195266 0.9676585
## 5: APC/C-mediated degradation of cell cycle proteins 0.6975169 0.9676585
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.9414141 0.9769392
## log2err ES NES size leadingEdge
## 1: 0.05641184 -0.6149068 -0.8169927 1 5687
## 2: 0.05641184 -0.6149068 -0.8169927 1 5687
## 3: 0.16957064 0.9254658 1.2522672 1 1432
## 4: 0.10395847 0.8385093 1.1346045 1 1213
## 5: 0.06831109 -0.5437500 -0.8514943 2 5687,983
## 6: 0.04889708 -0.5403727 -0.7179633 1 983## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_prep1, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: ABC transporter disorders 0.6827309 0.884146
## 2: ABC-family proteins mediated transport 0.6827309 0.884146
## 3: ADP signalling through P2Y purinoceptor 1 0.3194444 0.884146
## 4: ALK mutants bind TKIs 0.2718254 0.884146
## 5: APC/C-mediated degradation of cell cycle proteins 0.5010183 0.884146
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.5401606 0.884146
## log2err ES NES size leadingEdge
## 1: 0.06364241 -0.6645963 -0.8819656 1 5687
## 2: 0.06364241 -0.6645963 -0.8819656 1 5687
## 3: 0.10434395 0.8571429 1.1358118 1 1432
## 4: 0.11475072 0.8757764 1.1605033 1 1213
## 5: 0.07998588 -0.6687500 -1.0433186 2 983,5687
## 6: 0.07530938 -0.7329193 -0.9726350 1 983## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
EC vs E
data_diff_EC_vs_E_pY <- test_diff(pY_se_prep1, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_E_pY, contrast = "EC_vs_E", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: ABC transporter disorders 0.1949807 0.6159019
## 2: ABC-family proteins mediated transport 0.1949807 0.6159019
## 3: ADP signalling through P2Y purinoceptor 1 0.8533058 0.9397419
## 4: ALK mutants bind TKIs 0.3455598 0.7036098
## 5: APC/C-mediated degradation of cell cycle proteins 0.3657957 0.7036098
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.5540541 0.7475332
## log2err ES NES size leadingEdge
## 1: 0.13649044 -0.9068323 -1.2244229 1 5687
## 2: 0.13649044 -0.9068323 -1.2244229 1 5687
## 3: 0.05445560 0.5900621 0.7817798 1 1432
## 4: 0.09787733 -0.8136646 -1.0986260 1 1213
## 5: 0.10714024 -0.7187500 -1.1218142 2 5687,983
## 6: 0.07200331 -0.7142857 -0.9644427 1 983## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)EBC vs EC
data_diff_EC_vs_EBC_pY <- test_diff(pY_se_prep1, type = "manual",
test = c("EC_vs_EBC"))## Tested contrasts: EC_vs_EBCdep_EC_vs_EBC_pY <- add_rejections_SH(data_diff_EC_vs_EBC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_EBC_pY, contrast = "EC_vs_EBC", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_EBC_pY, comparison = "EC_vs_EBC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.9529412 0.9705882
## 2: ABC-family proteins mediated transport 0.9529412 0.9705882
## 3: ADP signalling through P2Y purinoceptor 1 0.1568627 0.5843293
## 4: ALK mutants bind TKIs 0.4552846 0.8378932
## 5: APC/C-mediated degradation of cell cycle proteins 0.8151571 0.9587068
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.5529412 0.8631660
## log2err ES NES size leadingEdge
## 1: 0.04697587 0.5279503 0.7111159 1 5687
## 2: 0.04697587 0.5279503 0.7111159 1 5687
## 3: 0.15524197 0.9192547 1.2381782 1 1432
## 4: 0.08504275 -0.7701863 -1.0319147 1 1213
## 5: 0.05142649 0.5312500 0.7977784 2 983,5687
## 6: 0.07289386 0.7142857 0.9620979 1 983#data_results <- get_df_long(dep)Prep 2
Each condition vs ctrl
pY_se_prep2 <- pY_se_preps
pY_se_prep2 <- pY_se_prep2[,pY_se_prep2$prep == "prep2" ]
data_diff_ctrl_vs_E_pY <- test_diff(pY_se_prep2, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modepaste0("log2FC_Xenograft_", colnames(pY_se_prep2) )## [1] "log2FC_Xenograft_ctrl_5d_7_Set1" "log2FC_Xenograft_ctrl_5d_5_Set1"
## [3] "log2FC_Xenograft_EBC_5d_4_Set2" "log2FC_Xenograft_ctrl_5d_7_Set2"
## [5] "log2FC_Xenograft_EC_5d_3_Set2" "log2FC_Xenograft_ctrl_5d_5_Set2"
## [7] "log2FC_Xenograft_E_5d_2_Set2" "log2FC_Xenograft_EBC_5d_3_Set2"
## [9] "log2FC_Xenograft_E_5d_3_Set2" "log2FC_Xenograft_EBC_5d_2_Set2"
## [11] "log2FC_Xenograft_E_5d_4_Set2" "log2FC_Xenograft_EC_5d_4_Set2"
## [13] "log2FC_Xenograft_E_5d_5_Set2" "log2FC_Xenograft_EC_5d_2_Set2"
## [15] "log2FC_Xenograft_EC_5d_1_Set2" "log2FC_Xenograft_EBC_5d_1_Set2"
## [17] "log2FC_Xenograft_EC_5d_5_Set2" "log2FC_Xenograft_EBC_5d_5_Set2"str_remove( colnames(pY_noNA), "log2FC_Xenograft_" )## [1] "Annotated_Sequence" "HGNC_Symbol"
## [3] "EC_24h_2_Set1" "EC_24h_5_Set1"
## [5] "E_24h_4_Set1" "ctrl_5d_7_Set1"
## [7] "EC_24h_1_Set1" "EBC_24h_3_Set1"
## [9] "ctrl_5d_3_Set1" "E_24h_1_Set1"
## [11] "E_24h_2_Set1" "EBC_24h_2_Set1"
## [13] "EBC_24h_4_Set1" "E_24h_5_Set1"
## [15] "E_24h_3_Set1" "ctrl_5d_5_Set1"
## [17] "EC_24h_4_Set1" "EC_24h_3_Set1"
## [19] "EBC_24h_1_Set1" "EBC_5d_4_Set2"
## [21] "ctrl_5d_7_Set2" "EC_5d_3_Set2"
## [23] "ctrl_5d_5_Set2" "E_5d_2_Set2"
## [25] "EBC_5d_3_Set2" "E_5d_3_Set2"
## [27] "EBC_5d_2_Set2" "ctrl_5d_3_Set2"
## [29] "E_5d_4_Set2" "EC_5d_4_Set2"
## [31] "E_5d_5_Set2" "EC_5d_2_Set2"
## [33] "EC_5d_1_Set2" "EBC_5d_1_Set2"
## [35] "EC_5d_5_Set2" "EBC_5d_5_Set2"
## [37] "Master.Protein.Accessions" "Master.Protein.Descriptions"pY_noNA %>% select()## # A tibble: 231 × 0Return_DEP_Hits_Plots(data = pY_noNA ,
dep_ctrl_vs_E_pY,
comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.66666667 0.9671838
## 2: ABC-family proteins mediated transport 0.66666667 0.9671838
## 3: ADP signalling through P2Y purinoceptor 1 0.90419162 0.9671838
## 4: ALK mutants bind TKIs 0.66866267 0.9671838
## 5: APC/C-mediated degradation of cell cycle proteins 0.37547893 0.9671838
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.07984032 0.8735720
## log2err ES NES size leadingEdge
## 1: 0.06450312 -0.6645963 -0.8858936 1 5687
## 2: 0.06450312 -0.6645963 -0.8858936 1 5687
## 3: 0.05019343 0.5527950 0.7392518 1 1432
## 4: 0.06435834 0.6645963 0.8887634 1 1213
## 5: 0.09255289 -0.6894069 -1.0702955 2 983
## 6: 0.22496609 -0.9565217 -1.2750244 1 983Return_DEP_Hits_Plots(data = pY_noNA ,
dep_ctrl_vs_E_pY,
comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.6731898 0.968303
## 2: ABC-family proteins mediated transport 0.6731898 0.968303
## 3: ADP signalling through P2Y purinoceptor 1 0.9164969 0.968303
## 4: ALK mutants bind TKIs 0.7189409 0.968303
## 5: APC/C-mediated degradation of cell cycle proteins 0.3865385 0.968303
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.1017613 0.968303
## log2err ES NES size leadingEdge
## 1: 0.06307904 -0.6645963 -0.8878223 1 5687
## 2: 0.06307904 -0.6645963 -0.8878223 1 5687
## 3: 0.05049830 0.5527950 0.7258897 1 1432
## 4: 0.06184060 0.6645963 0.8726988 1 1213
## 5: 0.09110731 -0.6894069 -1.0778980 2 983
## 6: 0.19578900 -0.9565217 -1.2778004 1 983data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_prep2, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.5637066 0.7433930
## 2: ABC-family proteins mediated transport 0.5637066 0.7433930
## 3: ADP signalling through P2Y purinoceptor 1 1.0000000 1.0000000
## 4: ALK mutants bind TKIs 0.9555985 0.9786249
## 5: APC/C-mediated degradation of cell cycle proteins 0.5943396 0.7622874
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.4090909 0.7000087
## log2err ES NES size leadingEdge
## 1: 0.07113274 0.6956522 0.9368367 1 5687
## 2: 0.07113274 0.6956522 0.9368367 1 5687
## 3: 0.04716425 -0.5031056 -0.6700750 1 1432
## 4: 0.04613792 0.5279503 0.7109921 1 1213
## 5: 0.05859376 -0.5848001 -0.9250701 2 983
## 6: 0.09196861 -0.7950311 -1.0588840 1 983
## Note: Row-scaling applied for this heatmap
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_prep2, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.1401674 0.3733803
## 2: ABC-family proteins mediated transport 0.1401674 0.3733803
## 3: ADP signalling through P2Y purinoceptor 1 0.9790076 0.9860967
## 4: ALK mutants bind TKIs 0.3053435 0.5396903
## 5: APC/C-mediated degradation of cell cycle proteins 0.1791908 0.4096414
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.5543933 0.6731919
## log2err ES NES size leadingEdge
## 1: 0.17093234 0.9316770 1.2399487 1 5687
## 2: 0.17093234 0.9316770 1.2399487 1 5687
## 3: 0.04450705 -0.5155280 -0.6765953 1 1432
## 4: 0.10473282 -0.8571429 -1.1249416 1 1213
## 5: 0.17821987 0.7375000 1.2521044 2 5687
## 6: 0.07608372 0.7329193 0.9754263 1 983EC vs E
data_diff_EC_vs_E_pY <- test_diff(pY_se_prep2, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_E_pY, contrast = "EC_vs_E", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.22515213 0.4323405
## 2: ABC-family proteins mediated transport 0.22515213 0.4323405
## 3: ADP signalling through P2Y purinoceptor 1 0.90373281 0.9515655
## 4: ALK mutants bind TKIs 0.96957404 0.9832448
## 5: APC/C-mediated degradation of cell cycle proteins 0.04223675 0.3014608
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.21095335 0.4323405
## log2err ES NES size leadingEdge
## 1: 0.12944289 0.8881988 1.1814610 1 5687
## 2: 0.12944289 0.8881988 1.1814610 1 5687
## 3: 0.04949049 -0.5527950 -0.7327596 1 1432
## 4: 0.04773424 0.5093168 0.6774811 1 1213
## 5: 0.32177592 0.8937500 1.4579291 2 983,5687
## 6: 0.13427345 0.8944099 1.1897229 1 983
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)EBC vs EC
data_diff_EC_vs_EBC_pY <- test_diff(pY_se_prep2, type = "manual",
test = c("EC_vs_EBC"))## Tested contrasts: EC_vs_EBCdep_EC_vs_EBC_pY <- add_rejections_SH(data_diff_EC_vs_EBC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_EBC_pY, contrast = "EC_vs_EBC", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_EBC_pY, comparison = "EC_vs_EBC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.14951456 0.4224357
## 2: ABC-family proteins mediated transport 0.14951456 0.4224357
## 3: ADP signalling through P2Y purinoceptor 1 0.94951456 0.9713305
## 4: ALK mutants bind TKIs 0.16016427 0.4224357
## 5: APC/C-mediated degradation of cell cycle proteins 0.02098680 0.3567757
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.06407767 0.4220706
## log2err ES NES size leadingEdge
## 1: 0.1585141 -0.9316770 -1.2389125 1 5687
## 2: 0.1585141 -0.9316770 -1.2389125 1 5687
## 3: 0.0466948 -0.5341615 -0.7103098 1 1432
## 4: 0.1574029 0.9130435 1.2236153 1 1213
## 5: 0.3524879 -0.9375000 -1.5339514 2 983,5687
## 6: 0.2489111 -0.9689441 -1.2884690 1 983
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)Tyrosine 24h
Each condition vs ctrl
pY_se_24h <- pY_se
pY_se_24h <- pY_se_24h[,pY_se_24h$day == "24h" | (pY_se_24h$day == "5d" & pY_se_24h$condition == "ctrl")]
data_diff_ctrl_vs_E_pY <- test_diff(pY_se_24h, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.5380117 0.8623487
## 2: ABC-family proteins mediated transport 0.5380117 0.8623487
## 3: ADP signalling through P2Y purinoceptor 1 0.2553606 0.8169134
## 4: ALK mutants bind TKIs 0.6298569 0.8984595
## 5: APC/C-mediated degradation of cell cycle proteins 0.6138614 0.8954140
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.7586912 0.9229336
## log2err ES NES size leadingEdge
## 1: 0.07399014 0.7391304 0.9875199 1 5687
## 2: 0.07399014 0.7391304 0.9875199 1 5687
## 3: 0.11776579 0.8695652 1.1617881 1 1432
## 4: 0.06847149 -0.6956522 -0.9225576 1 1213
## 5: 0.05934877 -0.5903616 -0.9166824 2 983
## 6: 0.05947603 -0.6335404 -0.8401863 1 983
## Note: Row-scaling applied for this heatmap
data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_24h, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.8775510 0.9535147
## 2: ABC-family proteins mediated transport 0.8775510 0.9535147
## 3: ADP signalling through P2Y purinoceptor 1 0.2578125 0.8872969
## 4: ALK mutants bind TKIs 0.3020408 0.8901872
## 5: APC/C-mediated degradation of cell cycle proteins 0.6819788 0.9295192
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.4755102 0.8901872
## log2err ES NES size leadingEdge
## 1: 0.05258990 -0.5652174 -0.7495957 1 5687
## 2: 0.05258990 -0.5652174 -0.7495957 1 5687
## 3: 0.11724972 0.8881988 1.1662384 1 1432
## 4: 0.10968406 -0.8447205 -1.1202749 1 1213
## 5: 0.05760911 -0.5687500 -0.8653430 2 983,5687
## 6: 0.08289621 -0.7639752 -1.0131898 1 983Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_24h, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.8264300 0.9124368
## 2: ABC-family proteins mediated transport 0.8264300 0.9124368
## 3: ADP signalling through P2Y purinoceptor 1 0.6242424 0.9021123
## 4: ALK mutants bind TKIs 0.3570020 0.8626744
## 5: APC/C-mediated degradation of cell cycle proteins 0.5987055 0.8862559
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.1715976 0.7302463
## log2err ES NES size leadingEdge
## 1: 0.05378728 -0.6024845 -0.8034639 1 5687
## 2: 0.05378728 -0.6024845 -0.8034639 1 5687
## 3: 0.06831109 0.6956522 0.9233491 1 1432
## 4: 0.09721508 -0.8198758 -1.0933735 1 1213
## 5: 0.05960370 -0.6062500 -0.9410346 2 983,5687
## 6: 0.14826150 -0.9068323 -1.2093373 1 983
## Note: Row-scaling applied for this heatmap
Not ctrl vs ctrl
data_diff_vs_ctrl_pY <- test_diff_to_all_other(pY_se_24h, control = "ctrl")
dep_vs_ctrl_pY <- add_rejections_SH(data_diff_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_vs_ctrl_pY, contrast = "not_ctrl_vs_ctrl", label_size = 2, add_names = TRUE, additional_title = "pY"))Return_DEP_Hits_Plots(data = pY_noNA, dep_vs_ctrl_pY, comparison = "not_ctrl_vs_ctrl_diff")EC vs E
data_diff_EC_vs_E_pY <- test_diff(pY_se_24h, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_E_pY, contrast = "EC_vs_E", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.2453222 0.6945422
## 2: ABC-family proteins mediated transport 0.2453222 0.6945422
## 3: ADP signalling through P2Y purinoceptor 1 0.8291747 0.9164046
## 4: ALK mutants bind TKIs 0.4012474 0.7575017
## 5: APC/C-mediated degradation of cell cycle proteins 0.3421687 0.7157219
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.5758836 0.7659333
## log2err ES NES size leadingEdge
## 1: 0.12503337 -0.9068323 -1.1952721 1 5687
## 2: 0.12503337 -0.9068323 -1.1952721 1 5687
## 3: 0.05237591 0.5900621 0.7856359 1 1432
## 4: 0.09344492 -0.8136646 -1.0724702 1 1213
## 5: 0.11237852 -0.7187500 -1.1342458 2 5687,983
## 6: 0.07380527 -0.7142857 -0.9414815 1 983#data_results <- get_df_long(dep)EBC vs EC
data_diff_EC_vs_EBC_pY <- test_diff(pY_se_24h, type = "manual",
test = c("EC_vs_EBC"))## Tested contrasts: EC_vs_EBCdep_EC_vs_EBC_pY <- add_rejections_SH(data_diff_EC_vs_EBC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_EBC_pY, contrast = "EC_vs_EBC", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_EBC_pY, comparison = "EC_vs_EBC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.9540230 0.9738117
## 2: ABC-family proteins mediated transport 0.9540230 0.9738117
## 3: ADP signalling through P2Y purinoceptor 1 0.1685824 0.6051969
## 4: ALK mutants bind TKIs 0.4895833 0.8881421
## 5: APC/C-mediated degradation of cell cycle proteins 0.8163636 0.9722946
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.5842912 0.9044905
## log2err ES NES size leadingEdge
## 1: 0.04586203 0.5279503 0.7004350 1 5687
## 2: 0.04586203 0.5279503 0.7004350 1 5687
## 3: 0.14733121 0.9192547 1.2195809 1 1432
## 4: 0.08243441 -0.7701863 -1.0052126 1 1213
## 5: 0.05060042 0.5312500 0.7935850 2 983,5687
## 6: 0.06895674 0.7142857 0.9476473 1 983#data_results <- get_df_long(dep)Preps
pY_se_24h_preps <- pY_se_24h
pY_se_24h_preps$treatment <- pY_se_24h_preps$condition
pY_se_24h_preps$prep <- unlist(prep_l[paste0("Xenograft_", rownames(colData(pY_se_24h_preps)) )])
pY_se_24h_preps_comp <- pY_se_24h_preps
pY_se_24h_preps_comp$condition <- pY_se_24h_preps$prep
data_diff_prep1_vs_prep2_pY <- test_diff(pY_se_24h_preps_comp, type = "manual",
test = c("prep1_vs_prep2"))## Tested contrasts: prep1_vs_prep2dep_prep1_vs_prep2_pY <- add_rejections_SH(data_diff_prep1_vs_prep2_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_prep1_vs_prep2_pY, contrast = "prep1_vs_prep2", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_prep1_vs_prep2_pY, comparison = "prep1_vs_prep2_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.8785425 0.9314468
## 2: ABC-family proteins mediated transport 0.8785425 0.9314468
## 3: ADP signalling through P2Y purinoceptor 1 0.7429150 0.9314468
## 4: ALK mutants bind TKIs 0.6259843 0.9275678
## 5: APC/C-mediated degradation of cell cycle proteins 0.7388633 0.9314468
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.3602362 0.7105925
## log2err ES NES size leadingEdge
## 1: 0.05216303 0.5590062 0.7381345 1 5687
## 2: 0.05216303 0.5590062 0.7381345 1 5687
## 3: 0.05998925 0.6459627 0.8529554 1 1432
## 4: 0.06689663 -0.7018634 -0.9278796 1 1213
## 5: 0.04744832 -0.5264515 -0.8246236 2 983
## 6: 0.09656296 -0.8322981 -1.1003174 1 983#data_results <- get_df_long(dep)Runs
pY_se_24h_runs <- pY_se_24h
pY_se_24h_runs$treatment <- pY_se_24h_runs$condition
pY_se_24h_runs$run <- str_split(colnames(pY_se_24h_runs), pattern = "_", simplify = TRUE)[,4]
pY_se_24h_runs_comp <- pY_se_24h_runs
pY_se_24h_runs_comp$condition <- pY_se_24h_runs$run
data_diff_Set1_vs_Set2_pY <- test_diff(pY_se_24h_runs_comp, type = "manual",
test = c("Set1_vs_Set2"))## Tested contrasts: Set1_vs_Set2dep_Set1_vs_Set2_pY <- add_rejections_SH(data_diff_Set1_vs_Set2_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_Set1_vs_Set2_pY, contrast = "Set1_vs_Set2", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_Set1_vs_Set2_pY, comparison = "Set1_vs_Set2_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: ABC transporter disorders 0.7256461 0.8738145
## 2: ABC-family proteins mediated transport 0.7256461 0.8738145
## 3: ADP signalling through P2Y purinoceptor 1 0.4048096 0.8457711
## 4: ALK mutants bind TKIs 0.2027833 0.8457711
## 5: APC/C-mediated degradation of cell cycle proteins 0.5122378 0.8457711
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.5149105 0.8457711
## log2err ES NES size leadingEdge
## 1: 0.06024841 -0.6335404 -0.8576597 1 5687
## 2: 0.06024841 -0.6335404 -0.8576597 1 5687
## 3: 0.09082414 0.7888199 1.0657343 1 1432
## 4: 0.13574094 -0.8819876 -1.1939969 1 1213
## 5: 0.07096095 -0.6375000 -0.9813769 2 983,5687
## 6: 0.07727470 -0.7329193 -0.9921946 1 983## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)Tyrosine 5d
Each condition vs ctrl
pY_se_5d <- pY_se
pY_se_5d <- pY_se_5d[,pY_se_5d$day == "5d" ]
data_diff_ctrl_vs_E_pY <- test_diff(pY_se_5d, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: ABC transporter disorders 0.6114519 0.8809053
## 2: ABC-family proteins mediated transport 0.6114519 0.8809053
## 3: ADP signalling through P2Y purinoceptor 1 0.6413255 0.8925227
## 4: ALK mutants bind TKIs 0.7582846 0.9408976
## 5: APC/C-mediated degradation of cell cycle proteins 0.4456522 0.8809053
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.2044990 0.8809053
## log2err ES NES size leadingEdge
## 1: 0.06994587 -0.6770186 -0.9105569 1 5687
## 2: 0.06994587 -0.6770186 -0.9105569 1 5687
## 3: 0.06523531 0.6770186 0.9054008 1 1432
## 4: 0.05724611 0.6273292 0.8389494 1 1213
## 5: 0.08998608 -0.6812500 -1.0671793 2 983,5687
## 6: 0.13725078 -0.9006211 -1.2112912 1 983## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_5d, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.7558140 0.8585859
## 2: ABC-family proteins mediated transport 0.7558140 0.8585859
## 3: ADP signalling through P2Y purinoceptor 1 0.4786822 0.7573016
## 4: ALK mutants bind TKIs 0.8953488 0.9416773
## 5: APC/C-mediated degradation of cell cycle proteins 0.6052174 0.8523120
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.4835391 0.7573016
## log2err ES NES size leadingEdge
## 1: 0.05712585 0.6273292 0.8372771 1 5687
## 2: 0.05712585 0.6273292 0.8372771 1 5687
## 3: 0.07977059 0.7453416 0.9947846 1 1432
## 4: 0.04929177 0.5590062 0.7460885 1 1213
## 5: 0.06252374 -0.5988760 -0.9159559 2 983
## 6: 0.08243441 -0.7577640 -1.0132543 1 983
## Note: Row-scaling applied for this heatmap
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_5d, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
label_size = 2, add_names = TRUE,
additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.1832669 0.4050936
## 2: ABC-family proteins mediated transport 0.1832669 0.4050936
## 3: ADP signalling through P2Y purinoceptor 1 0.6792829 0.7588166
## 4: ALK mutants bind TKIs 0.2340000 0.4707322
## 5: APC/C-mediated degradation of cell cycle proteins 0.1213720 0.4050936
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.4422311 0.6117320
## log2err ES NES size leadingEdge
## 1: 0.14375899 0.9192547 1.2193991 1 5687
## 2: 0.14375899 0.9192547 1.2193991 1 5687
## 3: 0.06350080 0.6583851 0.8733534 1 1432
## 4: 0.12563992 -0.8881988 -1.1764793 1 1213
## 5: 0.20895503 0.7937500 1.3474157 2 5687
## 6: 0.08553569 0.7888199 1.0463762 1 983Not ctrl vs ctrl
data_diff_vs_ctrl_pY <- test_diff_to_all_other(pY_se_5d, control = "ctrl")
dep_vs_ctrl_pY <- add_rejections_SH(data_diff_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_vs_ctrl_pY, contrast = "not_ctrl_vs_ctrl", label_size = 2, add_names = TRUE, additional_title = "pY"))Return_DEP_Hits_Plots(data = pY_noNA, dep_vs_ctrl_pY, comparison = "not_ctrl_vs_ctrl_diff")EC vs E
data_diff_EC_vs_E_pY <- test_diff(pY_se_5d, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_E_pY, contrast = "EC_vs_E", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.22630561 0.4248911
## 2: ABC-family proteins mediated transport 0.22630561 0.4248911
## 3: ADP signalling through P2Y purinoceptor 1 0.86391753 0.9158309
## 4: ALK mutants bind TKIs 0.96905222 0.9827157
## 5: APC/C-mediated degradation of cell cycle proteins 0.05733212 0.2883714
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.20889749 0.4248911
## log2err ES NES size leadingEdge
## 1: 0.12563992 0.8881988 1.193401 1 5687
## 2: 0.12563992 0.8881988 1.193401 1 5687
## 3: 0.05378728 -0.5527950 -0.752840 1 1432
## 4: 0.04558782 0.5093168 0.684328 1 1213
## 5: 0.32177592 0.8937500 1.475311 2 983,5687
## 6: 0.13145761 0.8944099 1.201747 1 983
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)EBC vs EC
data_diff_EC_vs_EBC_pY <- test_diff(pY_se_5d, type = "manual",
test = c("EC_vs_EBC"))## Tested contrasts: EC_vs_EBCdep_EC_vs_EBC_pY <- add_rejections_SH(data_diff_EC_vs_EBC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_EC_vs_EBC_pY, contrast = "EC_vs_EBC", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_EC_vs_EBC_pY, comparison = "EC_vs_EBC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.15637066 0.4453328
## 2: ABC-family proteins mediated transport 0.15637066 0.4453328
## 3: ADP signalling through P2Y purinoceptor 1 0.93436293 0.9723485
## 4: ALK mutants bind TKIs 0.16942149 0.4453328
## 5: APC/C-mediated degradation of cell cycle proteins 0.01316786 0.2527803
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.07335907 0.4425209
## log2err ES NES size leadingEdge
## 1: 0.15419097 -0.9316770 -1.2318518 1 5687
## 2: 0.15419097 -0.9316770 -1.2318518 1 5687
## 3: 0.04716425 -0.5341615 -0.7062617 1 1432
## 4: 0.15315881 0.9130435 1.2059003 1 1213
## 5: 0.38073040 -0.9375000 -1.5204521 2 983,5687
## 6: 0.23112671 -0.9689441 -1.2811259 1 983
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)Preps
pY_se_5d_preps <- pY_se_5d
pY_se_5d_preps$treatment <- pY_se_5d_preps$condition
pY_se_5d_preps$prep <- unlist(prep_l[paste0("Xenograft_", rownames(colData(pY_se_5d_preps)) )])
pY_se_5d_preps_comp <- pY_se_5d_preps
pY_se_5d_preps_comp$condition <- pY_se_5d_preps$prep
data_diff_prep1_vs_prep2_pY <- test_diff(pY_se_5d_preps_comp, type = "manual",
test = c("prep1_vs_prep2"))## Tested contrasts: prep1_vs_prep2dep_prep1_vs_prep2_pY <- add_rejections_SH(data_diff_prep1_vs_prep2_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_prep1_vs_prep2_pY, contrast = "prep1_vs_prep2", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_prep1_vs_prep2_pY, comparison = "prep1_vs_prep2_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: ABC transporter disorders 0.7704591 0.8825486
## 2: ABC-family proteins mediated transport 0.7704591 0.8825486
## 3: ADP signalling through P2Y purinoceptor 1 0.1796407 0.8171742
## 4: ALK mutants bind TKIs 0.5848303 0.8825486
## 5: APC/C-mediated degradation of cell cycle proteins 0.6275304 0.8825486
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.8183633 0.8825486
## log2err ES NES size leadingEdge
## 1: 0.05760911 -0.6335404 -0.8372185 1 5687
## 2: 0.05760911 -0.6335404 -0.8372185 1 5687
## 3: 0.14551615 -0.9192547 -1.2147876 1 1432
## 4: 0.07096095 0.7142857 0.9486414 1 1213
## 5: 0.06815134 -0.6125000 -0.9173340 2 5687,983
## 6: 0.05479395 -0.6086957 -0.8043864 1 983## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)Runs
pY_se_5d_runs <- pY_se_5d
pY_se_5d_runs$treatment <- pY_se_5d_runs$condition
pY_se_5d_runs$run <- str_split(colnames(pY_se_5d_runs), pattern = "_", simplify = TRUE)[,4]
pY_se_5d_runs_comp <- pY_se_5d_runs
pY_se_5d_runs_comp$condition <- pY_se_5d_runs$run
data_diff_Set1_vs_Set2_pY <- test_diff(pY_se_5d_runs_comp, type = "manual",
test = c("Set1_vs_Set2"))## Tested contrasts: Set1_vs_Set2dep_Set1_vs_Set2_pY <- add_rejections_SH(data_diff_Set1_vs_Set2_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Wrapper_Volcano(plot_volcano_SH(dep_Set1_vs_Set2_pY, contrast = "Set1_vs_Set2", label_size = 2, add_names = TRUE, additional_title = "pY"))## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeReturn_DEP_Hits_Plots(data = pY_noNA, dep_Set1_vs_Set2_pY, comparison = "Set1_vs_Set2_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: ABC transporter disorders 0.3984375 0.6833390
## 2: ABC-family proteins mediated transport 0.3984375 0.6833390
## 3: ADP signalling through P2Y purinoceptor 1 0.6152344 0.8547461
## 4: ALK mutants bind TKIs 0.4571429 0.7446491
## 5: APC/C-mediated degradation of cell cycle proteins 0.7801932 0.9510830
## 6: APC/C:Cdc20 mediated degradation of Cyclin B 0.9551020 0.9724256
## log2err ES NES size leadingEdge
## 1: 0.09026355 -0.8136646 -1.0774518 1 5687
## 2: 0.09026355 -0.8136646 -1.0774518 1 5687
## 3: 0.06736239 -0.7080745 -0.9376298 1 1432
## 4: 0.08504275 0.7577640 1.0208070 1 1213
## 5: 0.06613262 -0.4812500 -0.7635609 2 5687,983
## 6: 0.04870109 0.5217391 0.7028507 1 983#data_results <- get_df_long(dep)Session Info
sessionInfo()## R version 4.1.3 (2022-03-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] forcats_0.5.2 stringr_1.4.1
## [3] dplyr_1.0.10 purrr_0.3.5
## [5] readr_2.1.3 tidyr_1.2.1
## [7] tibble_3.1.8 ggplot2_3.3.6
## [9] tidyverse_1.3.2 mdatools_0.13.0
## [11] SummarizedExperiment_1.24.0 GenomicRanges_1.46.1
## [13] GenomeInfoDb_1.30.1 MatrixGenerics_1.6.0
## [15] matrixStats_0.62.0 DEP_1.16.0
## [17] org.Hs.eg.db_3.14.0 AnnotationDbi_1.56.2
## [19] IRanges_2.28.0 S4Vectors_0.32.4
## [21] Biobase_2.54.0 BiocGenerics_0.40.0
## [23] fgsea_1.20.0
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 shinydashboard_0.7.2 proto_1.0.0
## [4] gmm_1.7 tidyselect_1.2.0 RSQLite_2.2.18
## [7] htmlwidgets_1.5.4 grid_4.1.3 BiocParallel_1.28.3
## [10] norm_1.0-10.0 munsell_0.5.0 codetools_0.2-18
## [13] preprocessCore_1.56.0 chron_2.3-58 DT_0.26
## [16] withr_2.5.0 colorspace_2.0-3 highr_0.9
## [19] knitr_1.40 rstudioapi_0.14 mzID_1.32.0
## [22] labeling_0.4.2 GenomeInfoDbData_1.2.7 pheatmap_1.0.12
## [25] bit64_4.0.5 farver_2.1.1 vctrs_0.5.0
## [28] generics_0.1.3 xfun_0.34 R6_2.5.1
## [31] doParallel_1.0.17 clue_0.3-62 MsCoreUtils_1.6.2
## [34] bitops_1.0-7 cachem_1.0.6 DelayedArray_0.20.0
## [37] assertthat_0.2.1 promises_1.2.0.1 scales_1.2.1
## [40] googlesheets4_1.0.1 gtable_0.3.1 affy_1.72.0
## [43] sandwich_3.0-2 rlang_1.0.6 mzR_2.28.0
## [46] GlobalOptions_0.1.2 lazyeval_0.2.2 gargle_1.2.1
## [49] impute_1.68.0 broom_1.0.1 BiocManager_1.30.19
## [52] yaml_2.3.6 modelr_0.1.9 crosstalk_1.2.0
## [55] backports_1.4.1 httpuv_1.6.6 tools_4.1.3
## [58] affyio_1.64.0 ellipsis_0.3.2 gplots_3.1.3
## [61] jquerylib_0.1.4 RColorBrewer_1.1-3 STRINGdb_2.6.5
## [64] MSnbase_2.20.4 gsubfn_0.7 Rcpp_1.0.9
## [67] hash_2.2.6.2 plyr_1.8.7 zlibbioc_1.40.0
## [70] RCurl_1.98-1.9 sqldf_0.4-11 GetoptLong_1.0.5
## [73] zoo_1.8-11 haven_2.5.1 ggrepel_0.9.1
## [76] cluster_2.1.4 fs_1.5.2 magrittr_2.0.3
## [79] data.table_1.14.4 circlize_0.4.15 reprex_2.0.2
## [82] reactome.db_1.77.0 googledrive_2.0.0 pcaMethods_1.86.0
## [85] mvtnorm_1.1-3 ProtGenerics_1.26.0 hms_1.1.2
## [88] mime_0.12 evaluate_0.17 xtable_1.8-4
## [91] XML_3.99-0.12 readxl_1.4.1 gridExtra_2.3
## [94] shape_1.4.6 compiler_4.1.3 KernSmooth_2.23-20
## [97] ncdf4_1.19 crayon_1.5.2 htmltools_0.5.3
## [100] later_1.3.0 tzdb_0.3.0 lubridate_1.8.0
## [103] DBI_1.1.3 dbplyr_2.2.1 ComplexHeatmap_2.10.0
## [106] MASS_7.3-58.1 tmvtnorm_1.5 Matrix_1.5-1
## [109] cli_3.4.1 vsn_3.62.0 imputeLCMD_2.1
## [112] parallel_4.1.3 igraph_1.3.5 pkgconfig_2.0.3
## [115] plotly_4.10.0 MALDIquant_1.21 xml2_1.3.3
## [118] foreach_1.5.2 bslib_0.4.0 XVector_0.34.0
## [121] rvest_1.0.3 digest_0.6.30 Biostrings_2.62.0
## [124] rmarkdown_2.17 cellranger_1.1.0 fastmatch_1.1-3
## [127] shiny_1.7.3 gtools_3.9.3 rjson_0.2.21
## [130] lifecycle_1.0.3 jsonlite_1.8.3 viridisLite_0.4.1
## [133] limma_3.50.3 fansi_1.0.3 pillar_1.8.1
## [136] lattice_0.20-45 KEGGREST_1.34.0 fastmap_1.1.0
## [139] httr_1.4.4 plotrix_3.8-2 glue_1.6.2
## [142] fdrtool_1.2.17 png_0.1-7 iterators_1.0.14
## [145] bit_4.0.4 stringi_1.7.8 sass_0.4.2
## [148] blob_1.2.3 caTools_1.18.2 memoise_2.0.1knitr::knit_exit()